Implementation

• A Bayesian Network is a directed acyclic graph:
  • A graph where the directions are links which indicate dependencies that exist between nodes.
  • Nodes represent propositions about events or events themselves.
  • Conditional probabilities quantify the strength of dependencies.

Consider the following example:

• The probability, that my car won't start.
• If my car won't start then it is likely that
  • The battery is flat or
  • The staring motor is broken.

In order to decide whether to fix the car myself or send it to the garage I make the following decision:

• If the headlights do not work then the battery is likely to be flat so i fix it myself.
• If the starting motor is defective then send car to garage.
• If battery and starting motor both gone send car to garage.

The network to represent this is as follows:

Fig. 21 A simple Bayesian network